Working Memory Load and Automaticity in Relation to STEM Undergraduates’ Mental Multiplication Performance
Abstract
STEM undergraduate programs in the United States face low retention rates and declining graduate school enrollment. Math performance is linked to these outcomes. Extant elementary student research reports the effects of working memory load (WML) and automaticity on mental multiplication performance, a skill central to STEM math. Thus, instructional designs that manage automaticity and WML have the potential to improve STEM math performance. This study examined the effects of high and low automaticity and WML on STEM undergraduates’ performance (accuracy and response time) on mental multiplication tasks by conducting two 2 (automaticity) × 2 (WML) repeated measures ANOVAs. To explore differences in impact on high and low achieving learners, two 2 (automaticity) × 2 (WML) × 2 (achievement) repeated measures ANOVAs were also conducted. Automaticity and WML difficulty were alternated across four conditions by adjusting structural characteristics of tasks. Participants included 68 undergraduate engineering students. Regardless of level of WML, students under high automaticity conditions performed faster and more accurately. Regardless of level of automaticity, students under low WML conditions performed faster and more accurately. While the effects of automaticity and WML on both accuracy and response time were significant, their effects were larger on response time. Group differences based on achievement were not significant. The simple effect of WML was larger under conditions with low automaticity compared to conditions with high automaticity for response time but not accuracy. Alternating difficulty levels on two dimensions of automaticity and WML did not lead to poorer performance by the lower achieving group.
Subject Area
Psychology|Educational psychology|Cognitive psychology
Recommended Citation
Trimm, Jolene, "Working Memory Load and Automaticity in Relation to STEM Undergraduates’ Mental Multiplication Performance" (2023). ETD Collection for Fordham University. AAI28319106.
https://research.library.fordham.edu/dissertations/AAI28319106
